(1) Field of the Invention
The present invention relates to the field of image processing techniques, and more particularly to a method for automatically classifying test images based on their similarities with a dictionary of example target and non-target images organized according to class.
(2) Description of Related Art
The use of automatic pattern recognition systems and image classifiers for rapid identification and classification of input patterns (images) into one of several classes is well known in the art. Image classifiers have both military and civilian applications. For example, such systems can be used by a military combatant in a naval conflict to identify an unknown sonar target as a friend or foe, and thereby enable one to make an informed decision as to whether to attack the target. The systems are also used by civilians, for example, in medical screening and diagnostic applications. Additionally, image classification techniques are used for quality control in manufacturing applications.
Existing pattern recognition and image classification systems are typically based upon one of several conventional classification techniques. The conventional techniques for classifying images typically use a minimum set of manually distilled classification parameters from examples of known images which have been experimentally demonstrated to accurately classify a database of images into the correct class. For example, in the case of statistical classifiers, these parameters (features) consist of statistical moments scored according to a threshold criteria or nearest neighbor criteria. The features may also be based on ad hoc measurements or values defining properties of the image to be classified which have been proven successful on a test database. Additionally, classification parameters may be based on a model of the mechanisms which distinguish a class of images. Such conventional methods are well known in the art with examples being found in U.S. Pat. No. 5,291,563 to Maeda, and U.S. Pat. No. 5,452,369 to Lionti et al.
In general, conventional automatic classifiers process a small set of clues derived from a large sequence of data representing the image to be classified. These conventional classification methods suffer from several significant drawbacks. One drawback is that the classification parameters or features used to classify an image are only a partial representation of the information in the image. Additionally, the methods are biased by the ad hoc algorithm used to quantitatively score the parameters used for classification. Furthermore, the existing techniques often are not easily modified for new or changing operational environments or when new input images or outcome classes are added. Often such changes require changing or modifying the features used for classification.
Accordingly, there is a need for a classification method which overcomes these drawbacks.